Effects of Side Information on Complexity Reduction in Superimposed Pilot Channel Estimation in OFDM Systems
Bibliographic record
Abstract
A novel, reduced complexity iterative channel estimation algorithm for OFDM systems using superimposed pilots is proposed. It utilizes past channel estimations of double correlated channel as a side information to reduce number of iterations. Since pilots are available at all positions of the time-frequency OFDM grid in superimposed technique, the performance of the channel estimation does not degrade because of the variations of the fast fading channel between two pilots. On the other hand since no subcarrier is reserved for channel estimation purpose, superimposed pilot technique leads to improved spectral efficiency comparing to in-band OFDM pilots. However interference from data carrying signals made channel estimation more complex. In this paper, Least Square (LS) channel estimation followed by two dimensional Wiener filter for reducing OFDM symbol interference is done iteratively to achieve the Minimum Mean Square Error (MMSE). Small variations of the channel over each OFDM symbol duration are neglected due to a high data rate, but the values between different OFDM symbols are assumed correlated. The channel is modeled as a double selective, i.e. both frequency selectivity channel and Doppler shift are taken into consideration. Past channel estimates are used as side information for the present channel estimation to improve the forthcoming channel estimation at the first iteration and reduce the total number of iterations required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".